Citation: Yang, Y.; Mei, G.
Pneumonia Recognition by Deep
Learning: A Comparative
Investigation. Appl. Sci. 2022, 12,
4334. https://doi.org/10.3390/
app12094334
Academic Editors: Keun Ho Ryu and
Nipon Theera-Umpon
Received: 24 March 2022
Accepted: 24 April 2022
Published: 25 April 2022
Publisher’s Note: MDPI stays neutral
with regard to jurisdictional claims in
published maps and institutional affil-
iations.
Copyright: © 2022 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
Article
Pneumonia Recognition by Deep Learning:
A Comparative Investigation
Yuting Yang and Gang Mei *
School of Engineering and Technology, China University of Geosciences (Beijing), Beijing 100083, China;
yuting.yang@email.cugb.edu.cn
* Correspondence: gang.mei@cugb.edu.cn
Abstract:
Pneumonia is a common infectious disease. Currently, the most common method of pneu-
monia identification is manual diagnosis by professional doctors, but the accuracy and identification
efficiency of this method is not satisfactory, and computer-aided diagnosis technology has emerged.
With the development of artificial intelligence, deep learning has also been applied to pneumonia
diagnosis and can achieve high accuracy. In this paper, we compare five deep learning models in dif-
ferent situations for pneumonia recognition. The objective was to employ five deep learning models to
identify pneumonia X-ray images and to compare and analyze them in different cases, thus screening
out the optimal model for each type of case to improve the efficiency of pneumonia recognition and
further apply it to the computer-aided diagnosis of pneumonia species. In the proposed framework:
(1) datasets are collected and processed, (2) five deep learning models for pneumonia recognition are
built, (3) the five models are compared, and the optimal model for each case is selected. The results
show that the LeNet5 and AlexNet models achieved better pneumonia recognition for small datasets,
while the MobileNet and ResNet18 models were more suitable for pneumonia recognition for large
datasets. The comparative analysis of each model under different situations can provide a deeper
understanding of the efficiency of each model in identifying pneumonia, thus making the practical
application and selection of deep learning models for pneumonia recognition more convenient.
Keywords: pneumonia recognition; deep learning; X-ray; CNN; transformer
1. Introduction
Pneumonia is a common infectious disease usually triggered by pathogenic infections,
such as bacteria or viruses [
1
,
2
]. If pneumonia is not promptly treated, it can pose a great
threat to the life and health of the patient [
3
,
4
]. Therefore, early identification of pneumonia
is very important for timely detection and treatment. Chest X-ray images are widely used in
the identification of pneumonia because of their affordability and the rapid film formation
of X-ray images [
5
,
6
]. Currently, the most common method for pneumonia identification is
manual identification by X-ray images by a physician or specialist sitting in the clinic [
7
].
This method is subjective, has high fluctuation in accuracy, relies heavily on the clinical
experience of the diagnosing physician and is less efficient [
8
,
9
]. In less-developed regions,
the lack of specialized doctors and specialized medical equipment prevents the timely
diagnosis of pneumonia. As a result, the mortality rate caused by pneumonia is high in
less-developed countries and regions, which seriously affects the life and health of people.
To improve the efficiency and accuracy of pneumonia recognition, computer-aided
pneumonia diagnosis techniques have been utilized. With the rapid development of deep
learning [
10
,
11
], this has been widely employed in the medical field [
12
], including for
pneumonia recognition [
13
]. Due to the high accuracy and robustness of deep learning,
the efficiency of pneumonia diagnosis has been greatly improved [14,15].
Various scholars have conducted extensive studies on the application of deep learning
in pneumonia recognition.
Appl. Sci. 2022, 12, 4334. https://doi.org/10.3390/app12094334 https://www.mdpi.com/journal/applsci
